1. Field of the Invention
This invention pertains to the field of rehabilitation patients with stroke. More particularly, the invention relates to methods and systems for rehabilitation patients with stroke.
2. Description of Related Art
Stroke is the leading cause of disability among American adults. Each year in the U.S., approximately 750,000 people suffer strokes and of those, nearly 400,000 survive with some level of neurological impairment and disability. Nearly three million people are affected by stroke-related disability, and the estimated financial burden is thirty billion dollars annually.
After stroke-hemiparesis or other brain lesions, one of the most important patient-centered goals is voluntary use of the paretic arm in daily life. Statistics indicate that over 80% of first-time strokes (infarctions only) involve acute hemiparesis of the upper limb that significantly affects the functional independence and health of the stroke survivor. Of all impairments that result from stroke, traditional rehabilitation methods are the least effective in treating hemiparesis of the upper limb. In addition, stroke often leaves individuals unable to perform functional movements with the impaired limb even though the limb is not completely paralyzed. Post-stroke individuals with relatively high upper extremity motor function often choose to perform daily activities with the less affected arm. In this regard, non-use has been defined as the difference between what a patient can do when forced to use a paretic arm and what the patient does when given a free choice to use either arm. This learned non-use, is most obvious during the early post-injury period but can improve with rehabilitation therapy (Nudo et al, 2001; Taub et al, 2003; Winstein et al, 2003). Limb choice is an important anticipatory pre-movement parameter that has received little attention from the neuro-rehabilitation research community.
Traditional methods of measuring use include the Motor Activity Log (MAL) and the Actual Amount of Use Test (AAUT). Such methods to measure non-use are not often practical, or even reproducible, given the nature of the tests. In the MAL, a participant is asked to rate the amount of use (AOU) and quality of movement (QOM) for the paretic arm over a specified period for each item. The MAL suffers from numerous drawbacks: 1) it relies on the participant's memory; 2) it takes at least one hour for a trained interviewer to conduct and score; 3) participants with discordant stroke may choose to not perform certain activities with the paretic hand simply because it is their non-dominant hand, thereby affecting the sensitivity of the MAL. In the AAUT, participants are asked to perform functional tasks while their performances are videotaped without the participants being aware of it. However, since the participants must be unaware of the videotaping, the test is time-consuming and difficult to administer repeatedly. Because we do not have good quick tests at present, therapists cannot accurately quantify progress of patient in normal clinic and cannot design effective individualized therapy. Other tests measure arm and hand function and impairments (e.g. Fugl-Meyer and WMFT), but are also lengthy and are not practical to administer given the often limited contact time between physical therapists and patients.
Recent clinical studies have found that intensive motor training can improve functional upper limb performance in patients. In particular, there is evidence that intensive task practice, in which patients actively engage in repeated attempts to produce motor behaviors beyond their present capabilities, is effective for improving upper extremity function after stroke (Butefisch, 1995; Kwakkel, 1999; Wolf, 2002). These data suggest that the potential for functional recovery after stroke lasts much longer than previously believed, and that the degree of recovery depends not only on the level of initial impairment but also on the amount, type, and intensity of task practice available to the patient during the recovery process.
In previous studies that demonstrated functional improvements after brain lesion and/or experience-dependent neuroplasticity show that a large amount of practice is needed to show improvement. In one experiment with monkeys, 9,600 pellet retrievals were carried out over 4 weeks (Nudo et al., 1996). In experiments with cats, 7,000 trials of a food catch task over 35 days were performed (Pavlides et al., 1993). In human studies, Doyou et al. (Doyon et al., 1997) trained their subjects for 2,400 repetitions of a 10 item sequence over 6 weeks, and Kami (Karni, 1995) reports 31,500 repetitions of a finger sequence task over 35 days.
The number of repetitions demonstrated by these studies is in dramatic contrast with the limited time spent by the typical stroke patient undergoing neurological rehabilitation in actual therapeutic activity. Lincoln and colleagues report that only 36 min per day is spent in contact with either a physical or an occupational therapist, and “in all settings [we] observed patients spent many hours doing nothing” (Lincoln et al., 1996). Further, (Mackey et al.; 1996) reports that patients spent 70% of the day in activities largely unrelated to a physical outcome and less than 20% of the day in activities that could potentially contribute to their recovery. Finally (Keith and Cowell, 1987) point out that patients spent 8.1% (39 minutes) of the day in physical therapy (but with an undetermined proportion of that doing practice).
Another problem with conventional medical rehabilitation models is that they are largely constrained by economic considerations (i.e., how many sessions the patient's health insurance will cover) and are therefore not adequate to maximize functional outcomes. Further, due to the belief that therapy is only marginally effective, health insurance companies often reject requests for rehabilitation past 3 months post stroke.
In view of the shortcomings of the conventional medical practice model, there is a growing interest in employing robotic technology for rehabilitation of upper extremity movements. The use of robotic systems for limb rehabilitation are known, but are provided such that the robot directly assists the movements of an impaired limb. Current robots do not retrain functional tasks such as those requiring tool and object reaching and manipulation with grasping. Instead, they retrain reach, but do so with robotic assistance. In particular, MIT-MANUS (Aisen et al., 1997), the mirror-image motion enabler robot (Burgar et al., 2000), the ARM-guide system (Reinkensmeyer et al., 2004), and the Bi-Manu-Track (Hesse et al., 2003) are conventional robot systems assisting the movements of the affected limb. Other recent developments include balancing assistance provided by the robot with active movement by the patient, e.g. (Kahn et al., 2004), and EMG triggered robots (Dipietro et al., 2005). Still others provide robots that focus on hand retraining (L. Dovat, O. Lambercy, R. Gassert, T. Maeder, T Milner, C. L. Teo, E. Burdet. HandCARE: a Cable-Actuated REhabilitation system to train hand function after stroke. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 16(6): 582-591, 2008; O. Lambercy, L. Dovat, R. Gassert, E. Burdet, C. L. Teo, T. Milner. A haptic knob for rehabilitation of hand function. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 15(3):356-366, 2007). These systems have been shown to be effective to some extent (Krebs et al., 1999a; Krebs et al., 1999b; Lum et al., 2002), and can be used with patients that have no or little residual movement capabilities. However, they are mostly limited to large research and clinical centers because they are expensive, complex to maintain, and require supervision and/or assistance to use. Importantly, these systems provide motor learning on the robot and not to everyday tasks. Outside the clinical setting, there is no robotic assistance available. Therefore, the effectiveness of conventional robots is limited. Other systems provide balancing assistance from a robot in conjunction with active movement by the patient. These solutions suffer from their expense, complexity and the need for well-trained supervision. These robotic systems may offer benefits adjunctive to motor learning, just as a therapist uses passive and active movement to enhance flexibility and strength of the peripheral structures of a limb. However, these systems do not address the need for active motor learning in the rehabilitative process through adaptive training on a plurality of functional tasks involving reach, grasping and manipulation.
Virtual reality (VR) systems are also known and allow users to practice reaching movements. Virtual reality (VR) systems have the advantages of lower price, 3D interactions, increased safety, and can easily embed motivation principles from computer games. VR system designed to enhance reaching movements in patients with stroke have been tested in small pilot studies (Merians et al., 2002; Piron et al., 2004; Stewart et al., 2007; Subramanian et al., 2007). However, VR systems require 3-D goggles or projection screens to create the illusion of a 3-D virtual world. Users of VR systems also require an adaptation phase in order to mentally map one's actions onto the virtual world. Simple VR, which is without the use of a robot, typically does not allow natural movement. For instance if reaching to a target in a VR system, the patient can inadvertently move their hand through the target or cross the target at high velocity, since there is no physical object or constraint. Therefore, there is difficulty in transferring motor learning in a VR environment to a real world environment with physical objects. This adaptation may especially be difficult for older stroke victims to overcome and some may not be able to perceive the virtual world without discomfort. Therefore, there still exists a need for effective systems and techniques to rehabilitate patients with neurological disorders such as strokes.